Predicting Carbon in Steel Industries

 

Predicting Carbon in Steel Industries through Machine Learning!

          Predicting carbon with high accuracy in steel steel-making Industry is a complex process. Deep learning algorithms have come forward recently to predict carbon and sulfur accurately to avoid excessive waste of finished products or undesired eventualities like recalling the product from markets to avoid loss of reputation. Indeed costs are heavy if such an event occurs.

          The carbon content of the substance not only decides its strength and its brittleness in the end product but also affects the workability of the metal.

          If an incorrectly graded piece of steel is subsequently worked into a part, it can break because of being too soft or too brittle which, in turn, will result in mechanical failure. Carbon is a very important element in steel since it allows the steel to be hardened by heat treatment. Only a small amount of carbon is needed to produce steel: up to 0.25% for low-carbon steel, 0.25-0.50% for medium-carbon steel, and 0.50-1.25% for high-carbon steel. Steel can contain up to 2% carbon, but over that amount, it is considered to be cast iron, in which the excess carbon forms graphite.

          Process parameters are dependent on the analysis of the input raw material. Since the RM used in steelmaking, does not have uniform chemistry, its variables control the process and determine the quality of the end product.

          Normally random checks are conducted and the process is decided for each batch which becomes problematic to get desired output. It requires control of the temperature of molten mass, heat time, oxygen rate and other parameters depending on the impurities and the variables present in the RM.

          At present, the regression model is considered to be the most useful which enables predicting future responses with small variations. Without testing the samples randomly in the lab, spectrometric constant checks and based on analysis deciding process parameters beforehand yield a desirable quality of the end product.

          It requires a quality algorithm considering sets and subsets of the variables in Raw materials. If scrap, iron ore, or sponge iron is to be used as raw material in steel making, the percentage of impurities varies from batch to batch on a big scale which becomes problematic to control them to the desired level in the furnaces.

          

          The melting period is the heart of Electric Arc Furnace (EAF) operations. The EAF has evolved into a highly efficient melting. Melting is accomplished by supplying energy to the furnace interior. This energy can be electrical or chemical. Electrical energy is supplied via the graphite electrodes and is usually the largest contributor in melting operations.

          Refining operations in the electric arc furnace have traditionally involved the removal of phosphorus, sulfur, aluminum, silicon, manganese and carbon from the steel. In recent times, dissolved gases, especially hydrogen and nitrogen, have been recognized as a major concern. Control of the metallic properties in the batch is important as it determines the properties of the final product. Oxygen reacts with aluminum, silicon and manganese to form metallic oxides, which are slag components. These metallics tend to react with oxygen before the carbon. They will also react with FeO resulting in a recovery of iron units to every batch.

          The complexity of the steel-making process and interactive interdependency of the various elements becomes a challenge for devising a Deep Machine Learning model. The backbone Neuron Network has to be used diligently when the ML model has to be implemented to get the best advantage of ML.

          Since the measurement of hot metal composition offline is not of much help, a technique for measuring these variables with a Soft Sensor based on Neural Networks provides optimum results. The process and output variables that include quantity and slag as well as their composition with respect to all the important constituents need to be trained. These process variables can be measured online and hence the soft sensor can be used to predict the output parameters.

          A supervisory control system based on the neural network estimator and an expert system has been found to substantially improve the hot metal quality with respect to impurities such as excessive or undesired elements like carbon, silicon and sulfur.

          The success of the Machine learning model depends on the quality and representativeness of data used for training. It’s crucial to ensure that the training dataset is diverse, covering a wide range of Steel compositions and associated carbon content values. Steel making industry is energy intensive and traditionally the efficiency of the furnaces ranges from 60 to 70% at the maximum. An increase in productivity by applying modern techniques can not only enhance production but can save on energy costs and can balance the input-output ratio to the optimum perfection.

          Hence, all leading steel manufacturers are now tending to move towards ML in the production processes. However, lack of domain knowledge while creating a most suitable ML Model or using the most suitable algorithm, without making it complex and without asking for huge and unreliable data while setting up the parameters to predict and control the process parameters for quality output optimization becomes a challenge as there always is the shortage of experts.

          The steel industry has much more to benefit from intelligent ML models than what are being applied now.

-Sanjay Sonawani